Sklearn Gradient Boosting Classifier

Gradient Boosting Classifier, builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage n_classes_ regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.

Type

ml-estimator

Class

fire.nodes.sklearn.NodeSklearnGradientBoostingClassifier

Fields

Name

Title

Description

targetCol

Target Column

The label column for model fitting

featureCols

Feature Columns

Feature columns of type - all numeric, boolean and vector

splitRatio

Split Ratio

Split Ratio

loss

Loss

The loss function to be optimized. ‘Deviance’ refers to deviance (= logistic regression) for classification with probabilistic outputs.

learning_rate

LearningRate

Learning rate shrinks the contribution of each tree by learning_rate.

n_estimators

NEstimators

The number of boosting stages to be run.

subsample

Subsample

The fraction of samples to be used for fitting the individual base learners.

criterion

Criterion

The function to measure the quality of a split.

min_samples_split

MinSamplesSplit

The minimum number of samples required to split an internal node.

min_samples_leaf

MinSamplesLeaf

The minimum number of samples required to be at a leaf node.

min_weight_fraction_leaf

MinWeightFractionLeaf

The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.

max_depth

MaxDepth

Maximum depth of the individual regression estimators.

min_impurity_decrease

MinImpurityDecrease

A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

random_state

RandomState

Controls the randomness of the bootstrapping of the samples used when building trees.

verbose

Verbose

Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency).

max_leaf_nodes

MaxLeafNodes

Default value is None i.e -1

warm_start

WarmStart

presort

Presort

validation_fraction

ValidationFraction

n_iter_no_change

NIterNoChange

Default value is None i.e -1

tol

Tol

confusionMatrix

Confusion Matrix

output_confusion_matrix_chart

Output Confusion Matrix Chart

whether to display confusion matrix chart.

cm_chart_title

Confusion Matrix Chart Title

Title name to display in Confusion Matrix Chart

cm_chart_description

Confusion Matrix Chart Description

Description to display in Confusion Matrix CHart

confusionMatrixTargetLegend

Confusion Matrix Target Legend

Legend name to display for Target in Confusion Matrix

confusionMatrixPredictedLabelLegend

Confusion Matrix PredictedLabel Legend

Legend name to display for Predicted Label in Confusion Matrix

confusionMatrixCountLegend

Confusion Matrix Count Legend

Legend name to display for Count in Confusion Matrix

path

Save Confusion Matrix Path

Save Confusion Matrix

Description

Confusion Matrix Description

confusionMatrixRowDescription

Confusion Matrix Outcome description

One can provide the business details of the outcome of the confusion matrix rows

ROC Curve

ROC Curve

output_roc_curve

Output ROC Curve

whether to display confusion matrix chart.

roc_title

ROC Curve Chart Title

Title name to display in ROC Curve Chart

roc_description

ROC Curve Chart Description

Add Description for ROC Curve Chart

xlabel

X Label

X label

ylabel

Y Label

Y Label